NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

arXiv:2604.18637v1 Announce Type: cross Abstract: Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Science Foundation in August 2025, we identify three fundamental capability gaps in current AI: the inability to interact with the physical world, inadequate learning that produces brittle systems, and unsustainable energy and data inefficiency. We describe the neuroscience principles that address each: co-design of body and controller, prediction through interaction, multi-scale learning with neuromodulatory control, hierarchical distributed architectures, and sparse event-driven computation. We present a research roadmap organized around these principles at near, mid, and long-term horizons. We argue that realizing this program requires a new generation of researchers trained across the boundary between neuroscience and engineering, and describe the institutional conditions: interdisciplinary training, hardware access, community standards, and ethics, needed to support them. We conclude that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening our understanding of biological neural computation.

Related

• EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface
• Universal statistical signatures of evolution in artificial intelligence architectures
• Beyond LLMs, Sparse Distributed Memory, and Neuromorphics <A Hyper-Dimensional SRAM-CAM 'VaCoAl' for Ultra-High Speed, Ultra-Low Power, and Low Cost>
• AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows

Source: arXiv cs.AI | 2026-04-22